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Short-timescale atomic rearrangements are fundamental to the kinetics of glasses and frequently dominated by one atom moving significantly (a rearrangement), while others relax only modestly. The rates and directions of such rearrangements (or hops) are dominated by the distributions of activation barriers ( Eact) for rearrangement for a single atom and how those distributions vary across the atoms in the system. We have used molecular dynamics simulations of Cu50Zr50 metallic glass below Tg in an isoconfigurational ensemble to catalog the ensemble of rearrangements from thousands of sites. The majority of atoms are strongly caged by their neighbors, but a tiny fraction has a very high propensity for rearrangement, which leads to a power-law variation in the cage-breaking probability for the atoms in the model. In addition, atoms generally have multiple accessible rearrangement vectors, each with its own Eact. However, atoms with lower Eact (or higher rearrangement rates) generally explored fewer possible rearrangement vectors, as the low Eact path is explored far more than others. We discuss how our results influence future modeling efforts to predict the rearrangement vector of a hopping atom.

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes and transmutation, high temperature and temperature gradients, stresses, and corrosive coolants. They also have a wide range of microstructural and chemical makeup, with multifaceted and often out-of-equilibrium interactions. Machine learning (ML) is increasingly being used to tackle these complex time-dependent interactions and aid researchers in developing models and making predictions, sometimes with better accuracy than traditional modeling that focuses on one or two parameters at a time. Conventional practices of acquiring new experimental data in nuclear materials research are often slow and expensive, limiting the opportunity for data-centric ML, but new methods are changing that paradigm. Here we review high-throughput computational and experimental data approaches, especially robotic experimentation and active learning that based on Gaussian process and Bayesian optimization. We show ML examples in structural materials ( e.g., reactor pressure vessel (RPV) alloys and radiation detecting scintillating materials) and highlight new techniques of high-throughput sample preparation and characterizations, and automated radiation/environmental exposures and real-time online diagnostics. This review suggests that ML models of material constitutive relations in plasticity, damage, and even electronic and optical responses to radiation are likely to become powerful tools as they develop. Finally, we speculate on how the recent trends in artificial intelligence (AI) and machine learning will soon make the utilization of ML techniques as commonplace as the spreadsheet curve-fitting practices of today.

Accurately quantifying swelling of alloys that have undergone irradiation is essential for understanding alloy performance in a nuclear reactor and critical for the safe and reliable operation of reactor facilities. However, typical practice is for radiation-induced defects in electron microscopy images of alloys to be manually quantified by domain-expert researchers. Here, we employ an end-to-end deep learning approach using the Mask Regional Convolutional Neural Network (Mask R-CNN) model to detect and quantify nanoscale cavities in irradiated alloys. We have assembled a database of labeled cavity images which includes 400 images, > 34 k discrete cavities, and numerous alloy compositions and irradiation conditions. We have evaluated both statistical (precision, recall, and F1 scores) and materials property-centric (cavity size, density, and swelling) metrics of model performance, and performed targeted analysis of materials swelling assessments. We find our model gives assessments of material swelling with an average (standard deviation) swelling mean absolute error based on random leave-out cross-validation of 0.30 (0.03) percent swelling. This result demonstrates our approach can accurately provide swelling metrics on a per-image and per-condition basis, which can provide helpful insight into material design (e.g., alloy refinement) and impact of service conditions (e.g., temperature, irradiation dose) on swelling. Finally, we find there are cases of test images with poor statistical metrics, but small errors in swelling, pointing to the need for moving beyond traditional classification-based metrics to evaluate object detection models in the context of materials domain applications.

The information content of atomic-resolution scanning transmission electron microscopy (STEM) images can often be reduced to a handful of parameters describing each atomic column, chief among which is the column position. Neural networks (NNs) are high performance, computationally efficient methods to automatically locate atomic columns in images, which has led to a profusion of NN models and associated training datasets. We have developed a benchmark dataset of simulated and experimental STEM images and used it to evaluate the performance of two sets of recent NN models for atom location in STEM images. Both models exhibit high performance for images of varying quality from several different crystal lattices. However, there are important differences in performance as a function of image quality, and both models perform poorly for images outside the training data, such as interfaces with large difference in background intensity. Both the benchmark dataset and the models are available using the Foundry service for dissemination, discovery, and reuse of machine learning models.

In this work, we revisit the fragile-to-strong transition (FTS) in the simulated BKS silica from the perspective of microscopic dynamics in an effort to elucidate the dynamical behaviors of fragile and strong glass-forming liquids. Softness, which is a machine-learned feature from local atomic structures, is used to predict the microscopic activation energetics and long-term dynamics. The FTS is found to originate from a change in the temperature dependence of the microscopic activation energetics. Furthermore, results suggest there are two diffusion channels with different energy barriers in BKS silica. The fast dynamics at high temperatures is dominated by the channel with small energy barriers ($, 9 pages, 5 figures, additional 6 pages of supplementary materials

In a current density versus temperature (J-T) (Miram) curve in thermionic electron emission, experimental measurements demonstrate there is a smooth transition between the exponential region and the saturated emission regions, which is sometimes referred to as the "roll-off" or "Miram curve knee". The shape of the Miram curve knee is an important figure of merit for thermionic vacuum cathodes. Specifically, cathodes with a sharp Miram curve knee at low temperature with a flat saturated emission current are typically preferred. Our previous work on modeling nonuniform thermionic emission revealed that the space charge effect and patch field effect are key pieces of physics which impact the shape of the Miram curve knee. This work provides a more complete understanding of the physical factors connecting these physical effects and their relative impact on the shape of the knee, including the smoothness, the temperature, and the flatness of the saturated emission current density. For our analyses, we use a periodic, equal-width striped ("zebra crossing") work function distribution as a model system and illustrate how the space charge and patch field effects restrict the emission current density near the Miram curve knee. The results indicate there are three main physical parameters which significantly impact the shape of the Miram curve. Such physical knowledge directly connects the patch size, work function values, anode-cathode voltage, and anode-cathode gap distance to the shape of the Miram curve, providing new understanding and a guide to the design of thermionic cathodes used as electron sources in vacuum electronic devices (VEDs).

This paper provides an accurate theoretical defect energy database for pure and Bi-containing III-V (III-V:Bi) materials and investigates efficient methods for high-throughput defect calculations based on corrections of results obtained with local and semi-local functionals. Point defects as well as nearest-neighbor and second-nearest-neighbor pair defects were investigated in charge states ranging from -5 to 5. Ga-V:Bi systems (GaP:Bi, GaAs:Bi, and GaSb:Bi) were thoroughly investigated with significantly slower, higher fidelity hybrid Heyd-Scuseria-Ernzerhof (HSE) and significantly faster, lower fidelity local density approximation (LDA) calculations. In both approaches spurious electrostatic interactions were corrected with the Freysoldt correction. The results were verified against available experimental results and used to assess the accuracy of a previous band alignment correction. Here, a modified band alignment method is proposed in order to better predict the HSE values from the LDA ones. The proposed method allows prediction of defect energies with values that approximate those from the HSE functional at the computational cost of LDA (about 20x faster for the systems studied here). Tests of selected point defects in In-V:Bi materials resulted in corrected LDA values having a mean absolute error (MAE)=0.175 eV for defect levels vs. HSE. The method was further verified on an external database of defects and impurities in CdX (X=S, Se, Te) systems, yielding a MAE=0.194 eV. These tests demonstrate the correction to be sufficient for qualitative and semi-quantitative predictions, and may suggest transferability to many semiconductor systems without significant loss in accuracy. Properties of the remaining In-V:Bi defects and all Al-V:Bi defects were predicted with the use of the modified band alignment method.

Experimental observations of thermionic electron emission demonstrate a smooth transition between TL and FSCL regions of the emitted-current-density-versus-temperature (J-T) (Miram) curve and the emitted-current-density-versus-voltage (J-V) curve. Knowledge of the temperature and shape of the TL-FSCL transition is important in evaluating the thermionic electron emission performance of cathodes, including predicting the lifetime. However, there have been no first-principles physics-based models that can predict the smooth TL-FSCL transition region for real thermionic cathodes without applying physically difficult to justify a priori assumptions or empirical phenomenological equations. Previous work detailing the nonuniform thermionic emission found that the effects of 3-D space charge, patch fields, and Schottky barrier lowering can lead to a smooth TL-FSCL transition region from a model thermionic cathode surface with a checkerboard spatial distribution of work function values. In this work, we construct a physics-based nonuniform emission model for commercial dispenser cathodes for the first time. This emission model is obtained by incorporating the cathode surface grain orientation via electron backscatter diffraction (EBSD) and the facet-orientation-specific work function values from density functional theory (DFT) calculations. The model enables construction of two-dimensional emitted current density maps of the cathode surface and corresponding J-T and J-V curves. The predicted emission curves show excellent agreement with experiment, not only in TL and FSCL regions but, crucially, also in the TL-FSCL transition region. This model improves the understanding on the relationship between thermionic emission and cathode microstructure, which is beneficial to the design of vacuum electronic devices.

MAST-SEY is an open-source Monte Carlo code capable of calculating secondary electron emission using input data generated entirely from first principle (density functional theory) calculations. It utilizes the complex dielectric function and Penn’s theory for inelastic scattering processes, and relativistic Schrodinger theory by means of a partial-wave expansion method to govern elastic scattering. It allows the user to include explicitly calculated momentum dependence of the dielectric function, as well as to utilize first-principle density of states in secondary electron generation, which provides a more complete description of the underlying physics. In this paper we thoroughly describe the theoretical aspects of the modeling, as used in the code, and present sample results obtained for copper and aluminum.

Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis. In this work, we discuss the application of machine learning approaches to find the location and geometry of different defect clusters in irradiated steels. We show that a deep learning based Faster R-CNN analysis system has a performance comparable to human analysis with relatively small training data sets. This study proves the promising ability to apply deep learning to assist the development of automated microscopy data analysis even when multiple features are present and paves the way for fast, scalable, and reliable analysis systems for massive amounts of modern electron microscopy data.

Videos captured using Transmission Electron Microscopy (TEM) can encode details regarding the morphological and temporal evolution of a material by taking snapshots of the microstructure sequentially. However, manual analysis of such video is tedious, error-prone, unreliable, and prohibitively time-consuming if one wishes to analyze a significant fraction of frames for even videos of modest length. In this work, we developed an automated TEM video analysis system for microstructural features based on the advanced object detection model called YOLO and tested the system on an in-situ ion irradiation TEM video of dislocation loops formed in a FeCrAl alloy. The system provides analysis of features observed in TEM including both static and dynamic properties using the YOLO-based defect detection module coupled to a geometry analysis module and a dynamic tracking module. Results show that the system can achieve human comparable performance with an F1 score of 0.89 for fast, consistent, and scalable frame-level defect analysis. This result is obtained on a real but exceptionally clean and stable data set and more challenging data sets may not achieve this performance. The dynamic tracking also enabled evaluation of individual defect evolution like per defect growth rate at a fidelity never before achieved using common human analysis methods. Our work shows that automatically detecting and tracking interesting microstructures and properties contained in TEM videos is viable and opens new doors for evaluating materials dynamics.

The enhancement of surface diffusion (DS) over the bulk (DV) in metallic glasses (MGs) is well documented and likely to strongly influence the properties of glasses grown by vapor deposition. Here, we use classical molecular dynamics simulations to identify different factors influencing the enhancement of surface diffusion in MGs. MGs have a simple atomic structure and belong to the category of moderately fragile glasses that undergo pronounced slowdown of bulk dynamics with cooling close to the glass transition temperature (Tg). We observe that DS exhibits a much more moderate slowdown compared to DV when approaching Tg, and DS/DV at Tg varies by two orders of magnitude among the MGs investigated. We demonstrate that both the surface energy and the fraction of missing bonds for surface atoms show good correlation to DS/DV, implying that the loss of nearest neighbors at the surface directly translates into higher mobility, unlike the behavior of network- and hydrogen-bonded organic glasses. Fragility, a measure of the slowdown of bulk dynamics close to Tg, also correlates to DS/DV, with more fragile systems having larger surface enhancement of mobility. The deviations observed in the fragility and DS over DV relationship are shown to be correlated to the extent of segregation or depletion of the mobile element at the surface. Finally, we explore the relationship between the diffusion pre-exponential factor (D0) and activation energy (Q) and compare to a ln(D0)-Q correlation previously established for bulk glasses, demonstrating similar correlations from MD as in the experiments and that the surface and bulk have very similar ln(D0)-Q correlations., Manuscript and Supplementary Information as a single PDF

Experimental observations have long-established that there exists a smooth roll-off or knee transition between the temperature-limited (TL) and full-space-charge-limited (FSCL) emission regions of the emission current density-temperature J-T (Miram) curve, or the emission current density-voltage J-V curve for a thermionic emission cathode. In this paper, we demonstrate that this experimentally observed smooth transition does not require frequently used a priori assumptions of a continuous distribution of work functions on the cathode surface. Instead, we find the smooth transition arises as a natural consequence of the physics of nonuniform thermionic emission from a spatially heterogeneous cathode surface. We obtain this smooth transition for both J-T and J-V curves using a predictive nonuniform thermionic emission model that includes 3-D space charge, patch fields (electrostatic potential nonuniformity on the cathode surface based on local work function values), and Schottky barrier lowering physics and illustrate that a smooth knee can arise from a thermionic cathode surface with as few as two discrete work function values. Importantly, we find that the inclusion of patch field effects is crucial for obtaining accurate J-T and J-V curves, and the further inclusion of Schottky barrier lowering is needed for accurate J-V curves. This finding, and the emission model provided in this paper have important implications for modeling electron emission from realistic, heterogeneous surfaces. Such modeling is important for improved understanding of the interplay of emission physics, cathode materials engineering, and design of numerous devices employing electron emission cathodes., IEEE Transactions on Electron Devices (2021)

The electronic, magnetic, thermoelectric, and topological properties of Heusler compounds (composition $XYZ$ or $X_2 YZ$) are highly sensitive to stoichiometry and defects. Here we establish the existence and experimentally map the bounds of a \textit{semi} adsorption-controlled growth window for semiconducting half Heusler FeVSb films, grown by molecular beam epitaxy (MBE). We show that due to the high volatility of Sb, the Sb stoichiometry is self-limiting for a finite range of growth temperatures and Sb fluxes, similar to the growth of III-V semiconductors such as GaSb and GaAs. Films grown within this window are nearly structurally indistinguishable by X-ray diffraction (XRD) and reflection high energy electron diffraction (RHEED). The highest electron mobility and lowest background carrier density are obtained towards the Sb-rich bound of the window, suggesting that Sb-vacancies may be a common defect. Similar \textit{semi} adsorption-controlled bounds are expected for other ternary intermetallics that contain a volatile species $Z=$\{Sb, As, Bi\}, e.g., CoTiSb, LuPtSb, GdPtBi, and NiMnSb. However, outstanding challenges remain in controlling the remaining Fe/V ($X/Y$) transition metal stoichiometry.

The slow kinetics of the oxygen reduction reaction (ORR) is one of the key challenges in developing high performance energy devices, such as solid oxide fuel cells. Straining a film by growing on a lattice-mismatched substrate has been a conventional approach to enhance the ORR activity. However, due to the limited choice of electrolyte substrates to alter the degree of strain, a systematic study in various materials has been a challenge. Here, we explore the strain modulation of the ORR kinetics by growing epitaxial La0.6Sr0.4CoO3-{\delta} (LSCO) films on yttria-stabilized zirconia substrates with the film thickness below and above the critical thickness for strain relaxation. Two orders of magnitude higher ORR kinetics is achieved in an ultra-thin film with ~0.8% tensile strain as compared to unstrained films. Time-of-flight secondary ion mass spectrometry depth profiling confirms that the Sr surface segregation is not responsible for the enhanced ORR in strained films. We attribute this enhancement of ORR kinetics to the increase in oxygen vacancy concentration in the tensile-strained LSCO film owing to the reduced activation barrier for oxygen surface exchange kinetics. Density functional theory calculations reveal an upshift of the oxygen 2p-band center relative to the Fermi level by tensile strain, indicating the origin of the enhanced ORR kinetics.

Materials that exhibit a low work function and therefore easily emit electrons into vacuum form the basis of electronic devices used in applications ranging from satellite communications to thermionic energy conversion. W-Ba-O is the canonical materials system that functions as the thermionic electron emitter used commercially in a range of high power electron devices. However, the work functions, surface stability, and kinetic characteristics of a polycrystalline W emitter surface are still not well understood or characterized. In this study, we examined the work function and surface stability of the eight lowest index surfaces of the W-Ba-O system using Density Functional Theory methods. We found that under the typical thermionic cathode operating conditions of high temperature and low oxygen partial pressure, the most stable surface adsorbates are Ba-O species with compositions in the range of Ba0.125O to Ba0.25O per surface W atom, with O passivating all dangling W bonds and Ba creating work function-lowering surface dipoles. Wulff construction analysis reveals that the presence of O and Ba significantly alters the surface energetics and changes the proportions of surface facets present under equilibrium conditions. Analysis of previously published data on W sintering kinetics suggests that fine W particles in the size range of 100-500 nm may be at or near equilibrium during cathode synthesis, and thus may exhibit surface orientation fractions well-described by the calculated Wulff construction.

We carried out molecular dynamics simulations (MD) using realistic empirical potentials for the vapor deposition (VD) of CuZrAl glasses. VD glasses have higher densities and lower potential and inherent structure energies than the melt-quenched glasses for the same alloys. The optimal substrate temperature for the deposition process is 0.625$\times T_\mathrm{g}$. In VD metallic glasses (MGs), the total number of icosahedral like clusters is higher than in the melt-quenched MGs. Surprisingly, the VD glasses have a lower degree of chemical mixing than the melt-quenched glasses. The reason for it is that the melt-quenched MGs can be viewed as frozen liquids, which means that their chemical order is the same as in the liquid state. In contrast, during the formation of the VD MGs, the absence of the liquid state results in the creation of a different chemical order with more Zr-Zr homonuclear bonds compared with the melt-quenched MGs. In order to obtain MGs from melt-quench technique with similarly low energies as in the VD process, the cooling rate during quenching would have to be many orders of magnitude lower than currently accessible to MD simulations. The method proposed in this manuscript is a more efficient way to create MGs by using MD simulations.

The current work combines the Cluster Dynamics (CD) technique and CALPHAD-based precipitation modeling to address the second phase precipitation in cold-worked (CW) 316 stainless steels (SS) under irradiation at 300-400 C. CD provides the radiation enhanced diffusion and dislocation evolution as inputs for the precipitation model. The CALPHAD-based precipitation model treats the nucleation, growth and coarsening of precipitation processes based on classical nucleation theory and evolution equations, and simulates the composition, size and size distribution of precipitate phases. We benchmark the model against available experimental data at fast reactor conditions (9.4 x 10^-7 dpa/s and 390 C) and then use the model to predict the phase instability of CW 316 SS under light water reactor (LWR) extended life conditions (7 x 10^-8 dpa/s and 275 C). The model accurately predicts the gamma-prime (Ni3Si) precipitation evolution under fast reactor conditions and that the formation of this phase is dominated by radiation enhanced segregation. The model also predicts a carbide volume fraction that agrees well with available experimental data from a PWR reactor but is much higher than the volume fraction observed in fast reactors. We propose that radiation enhanced dissolution and/or carbon depletion at sinks that occurs at high flux could be the main sources of this inconsistency. The integrated model predicts ~1.2% volume fraction for carbide and ~3.0% volume fraction for gamma-prime for typical CW 316 SS (with 0.054 wt.% carbon) under LWR extended life conditions. This work provides valuable insights into the magnitudes and mechanisms of precipitation in irradiated CW 316 SS for nuclear applications.

Under irradiation, SiC develops damage commonly referred to as black spot defects, which are speculated to be self-interstitial atom clusters. To understand the evolution of these defect clusters and their impacts (e.g., through radiation induced swelling) on the performance of SiC in nuclear applications, it is important to identify the cluster composition, structure, and shape. In this work the genetic algorithm code StructOpt was utilized to identify groundstate cluster structures in 3C-SiC. The genetic algorithm was used to explore clusters of up to ~30 interstitials of C-only, Si-only, and Si-C mixtures embedded in the SiC lattice. We performed the structure search using Hamiltonians from both density functional theory and empirical potentials. The thermodynamic stability of clusters was investigated in terms of their composition (with a focus on Si-only, C-only, and stoichiometric) and shape (spherical vs. planar), as a function of the cluster size (n). Our results suggest that large Si-only clusters are likely unstable, and clusters are predominantly C-only for n 10. The results imply that there is an evolution of the shape of the most stable clusters, where small clusters are stable in more spherical geometries while larger clusters are stable in more planar configurations. We also provide an estimated energy vs. size relationship, E(n), for use in future analysis., 30 pages, 8 figures

Understanding the structure of ZnO surface reconstructions and their resultant properties is crucial to the rational design of ZnO-containing devices ranging from optoelectronics to catalysts. Here, we are motivated by recent experimental work that showed a new surface reconstruction containing Zn vacancies ordered in a Zn(3 × 3) pattern in the subsurface of (0001)-O-terminated ZnO. Reconstruction with Zn vacancies on (0001)-O is surprising and counterintuitive because Zn vacancies enhance the surface dipole rather than reduce it. In this work, we show using density functional theory (DFT) that subsurface Zn vacancies can form on (0001)-O when coupled with adsorption of surface H and are in fact stable under a wide range of common conditions. We also show that these vacancies have a significant ordering tendency and that Sb-doping-created subsurface inversion domain boundaries (IDBs) enhance the driving force of Zn vacancy alignment into large domains of the Zn(3 × 3) reconstruction.

In this study we employ Density Functional Theory (DFT) methods to investigate the surface energy barrier for electron emission (surface barrier) and thermodynamic stability of Ba and Ba-O species adsorption under conditions of high temperature (approximately 1200 K) and low pressure (approximately $10^{-10}$ Torr) on the low index surfaces of bixbyite $Sc_2O_3$. The role of Ba in lowering the cathode surface barrier is investigated via adsorption of atomic Ba and Ba-O dimers, where the highest simulated dimer coverage corresponds to a single monolayer film of rocksalt BaO. The change of the surface barrier of a semiconductor due to adsorption of surface species is decomposed into two parts: a surface dipole component and doping component. The lowest surface barrier with atomic Ba on $Sc_2O_3$ was found to be 2.12 eV and 2.04 eV for the (011) and (111) surfaces at 3 and 1 Ba atoms per surface unit cell (0.250 and 0.083 Ba per surface O), respectively. The lowest surface barrier for Ba-O on $Sc_2O_3$ was found to be 1.21 eV on (011) for a 7 Ba-O dimer-per-unit-cell coverage (0.583 dimers per surface O). Generally, we found that Ba in its atomic form on $Sc_2O_3$ surfaces is not stable relative to bulk BaO, while Ba-O dimer coverages between 3 to 7 Ba-O dimers per (011) surface unit cell (0.250 to 0.583 dimers per surface O) produce stable structures relative to bulk BaO. Ba-O dimer adsorption on $Sc_2O_3$ (111) surfaces was found to be unstable versus BaO over the full range of coverages studied. Investigation of combined n-type doping and surface dipole modification showed that their effects interact to yield a reduction less than the two contributions would yield separately.

Perovskite oxides containing transition metals are promising materials in a wide range of electronic and electrochemical applications. However, neither their work function values nor an understanding of their work function physics have been established. Here, we predict the work function trends of a series of perovskite ($ABO_3$ formula) materials using Density Functional Theory, and show that the work functions of (001)-terminated AO- and $BO_2$-oriented surfaces can be described using concepts of electronic band filling, bond hybridization, and surface dipoles. The calculated range of AO ($BO_2$) work functions are 1.60-3.57 eV (2.99-6.87 eV). We find an approximately linear correlation ($R^2$ between 0.77-0.86, depending on surface termination) between work function and position of the oxygen 2p band center, which correlation enables both understanding and rapid prediction of work function trends. Furthermore, we identify $SrVO_3$ as a stable, low work function, highly conductive material. Undoped (Ba-doped) $SrVO_3$ has an intrinsically low AO-terminated work function of 1.86 eV (1.07 eV). These properties make $SrVO_3$ a promising candidate material for a new electron emission cathode for application in high power microwave devices, and as a potential electron emissive material for thermionic energy conversion technologies.

We demonstrate automated generation of diffusion databases from high-throughput density functional theory (DFT) calculations. A total of more than 230 dilute solute diffusion systems in Mg, Al, Cu, Ni, Pd, and Pt host lattices have been determined using multi-frequency diffusion models. We apply a correction method for solute diffusion in alloys using experimental and simulated values of host self-diffusivity. We find good agreement with experimental solute diffusion data, obtaining a weighted activation barrier RMS error of 0.176 eV when excluding magnetic solutes in non-magnetic alloys. The compiled database is the largest collection of consistently calculated ab-initio solute diffusion data in the world., Comment: 16 pages, 5 figures, submitted to Scientific Data. The diffusion dataset can be found at: http://dx.doi.org/10.6084/m9.figshare.1546772 http://diffusiondata.materialshub.org

To date, the preparation of free-standing 2D nanomaterials has been largely limited to the exfoliation of van der Waals solids. The lack of a robust mechanism for the bottom-up synthesis of 2D nanomaterials from non-layered materials has become an obstacle to further explore the physical properties and advanced applications of 2D nanomaterials. Here we demonstrate that surfactant monolayers can serve as soft templates guiding the nucleation and growth of 2D nanomaterials in large area beyond the limitation of van der Waals solids. One- to 2-nm-thick, single-crystalline free-standing ZnO nanosheets with sizes up to tens of micrometres are synthesized at the water–air interface. In this process, the packing density of surfactant monolayers adapts to the sub-phase metal ions and guides the epitaxial growth of nanosheets. It is thus named adaptive ionic layer epitaxy (AILE). The electronic properties of ZnO nanosheets and AILE of other materials are also investigated., The recently discovered phenomena arising from 2D nanomaterials have led to an increased interest in the fabrication of other ultrathin materials from those typically only observed in the bulk. Here, the authors demonstrate the synthesis of micron-sized, single-crystalline ZnO nanosheets via solution based methods.

The growth and properties of alloys in the alternative quaternary alloy system GaAs1−y−zPyBiz were explored. This materials system allows simultaneous and independent tuning of lattice constant and band gap energy, Eg, over a wide range for potential near- and mid-infrared optoelectronic applications by adjusting y and z in GaAs1−y−zPyBiz. Highly tensile-strained, pseudomorphic films of GaAs1−yPy with a lattice mismatch strain of ∼1.2% served as the host for the subsequent addition of Bi. Lattice-matched alloy materials to GaAs were generated by holding y ∼ 3.3z in GaAs1−y−zPyBiz. Epitaxial films with both high Bi content, z ∼ 0.0854, and a smooth morphology were realized with measured band gap energies as low as 1.11–1.01 eV, lattice-matched to GaAs substrates. Density functional theory calculations are used to provide a predictive model for the band gap of GaAs1−y−zPyBiz lattice-matched to GaAs.

Energy barriers of the key annealing reactions of neutral and charged point defects in SiC are calculated with ab initio density functional theory methods. In order to effectively search for the lowest energy migration paths the preliminary path is first established based on ab initio molecular dynamics (AIMD) simulations. The energy barrier of each hop is then calculated via climbing image nudged elastic band methods for paths guided by the AIMD simulations. The final paths and barriers are determined by comparing different pathways. The annealing reactions have important implications in understanding the amorphization, recovery, and other aspects of the radiation response of SiC. The results are compared with the literature data and experimental results on SiC recovery and amorphization. We propose that the C interstitial and Si antisite annealing reaction may provide a critical barrier that governs both the recovery stage III and amorphization processes. � 2013 American Physical Society.

Materials with the olivine LixMPO4 structure form an important new class of materials for rechargeable Li batteries. There is significant interest in their electronic properties because of the importance of electronic conductivity in batteries for high rate applications. The density of states of LixMPO4 (x = 0, 1 and M = Fe, Mn) has been determined with the ab initio GGA+U method, appropriate for these correlated electron systems. Computed results are compared with the optical gap of LiFePO4, as measured using UV-Vis-NIR diffuse reflectance spectroscopy. The results obtained from experiment (3.8-4.0 eV) and GGA+U computations (3.7 eV) are in very good agreement. However, standard GGA, without the same level of treatment of electron correlation, is shown to make large errors in predicting the electronic structure. It is argued that olivines are likely to be polaronic conductors with extrinsically determined carrier levels and that their electronic conductivity is therefore not simply related to the band gap., 17 pages, 2 figures